| Issue |
ITM Web Conf.
Volume 45, 2022
2021 3rd International Conference on Computer Science Communication and Network Security (CSCNS2021)
|
|
|---|---|---|
| Article Number | 01031 | |
| Number of page(s) | 7 | |
| Section | Computer Technology and System Design | |
| DOI | https://doi.org/10.1051/itmconf/20224501031 | |
| Published online | 19 May 2022 | |
Research on applicability test algorithm of wind and rain monitoring equipment of high-speed railway
1
China Academy of Railway Sciences, 2 Daliushu Road, Haidian District, Beijing, China
2
China Academy of Railway Sciences Corporation Limited, Institute of Computing Technologies, 2 Daliushu Road, Haidian District, Beijing, China
3
Qamdo Meteorological Bureau, 8 North Qamdo Road, Qamdo, China
* Corresponding author: lwtcon@163.com
High-speed railway natural disaster and foreign object intrusion monitoring system is an important technical guarantee for the safe operation of trains. As the basic data source, the reliability and stability of meteorological monitoring equipment is an important prerequisite for the system to play a role. Due to the special measurement principle of meteorological monitoring equipment and the inconvenience of equipment inspection along the high-speed railway, there is currently a lack of flexible and efficient applicability inspection algorithms for equipment failure and monitoring data distortion. Based on the measurement principle of the wind and rain monitoring equipment, this paper analyzes the causes of data distortion and abnormal data characteristics, then puts forward the applicability test algorithm of high-speed railway wind and rain monitoring equipment. Through data analysis methods such as correlation test and difference test, the applicability test of anemometers was carried out. The top-cover piezoelectric rain gauge inspection equipment was developed, and the applicability test of microwave rain gauge was carried out by means of data comparative analysis. Finally, we tested the algorithm in actual high-speed rail lines, and the results show that the proposed algorithm can effectively identify the monitoring equipment with poor adaptability.
© The Authors, published by EDP Sciences, 2022
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